From 87e896a46a9403813654cadd609960c3b2af87be Mon Sep 17 00:00:00 2001 From: Giuseppe Rossini Date: Fri, 24 Aug 2018 10:24:12 +0100 Subject: [COMPMID-1353] Add support for 4D Softmax layer on OpenCL Change-Id: I4342d4240fe5b1aab234c015684a1216c3990a5f Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/145631 Tested-by: Jenkins Reviewed-by: Anthony Barbier Reviewed-by: Georgios Pinitas --- src/runtime/CL/functions/CLSoftmaxLayer.cpp | 99 +++++++++++++++++++++++++---- 1 file changed, 88 insertions(+), 11 deletions(-) (limited to 'src/runtime/CL/functions/CLSoftmaxLayer.cpp') diff --git a/src/runtime/CL/functions/CLSoftmaxLayer.cpp b/src/runtime/CL/functions/CLSoftmaxLayer.cpp index 7a20d9f94b..3a7d6c770b 100644 --- a/src/runtime/CL/functions/CLSoftmaxLayer.cpp +++ b/src/runtime/CL/functions/CLSoftmaxLayer.cpp @@ -29,14 +29,32 @@ #include "arm_compute/core/Helpers.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Utils.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/CL/CLMemoryGroup.h" #include "arm_compute/runtime/CL/CLScheduler.h" -using namespace arm_compute; - +namespace arm_compute +{ CLSoftmaxLayer::CLSoftmaxLayer(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _max_shift_exp_sum_kernel(), _norm_kernel(), _max(), _sum(), _tmp() + : _memory_group(std::move(memory_manager)), _max_shift_exp_sum_kernel(), _norm_kernel(), _flatten_kernel(), _reshape_kernel(), _max(), _sum(), _tmp(), _input_flat(), _output_flat(), + _needs_flattening(false) +{ +} + +void CLSoftmaxLayer::configure_flatten_kernel(const ICLTensor *input, const ICLTensor *output) { + // Flatten the input + const TensorShape shape_flatten = misc::shape_calculator::compute_flatten_shape(input->info()); + + // Initialize the flat input + _input_flat.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten)); + + // Configure the flatten_kernel + _flatten_kernel.configure(input, &_input_flat); + + // We need to init the output tensor here. Indeed, the reshape kernel expects + // both tensors to be already initialized + auto_init_if_empty(*output->info(), *input->info()->clone()); } void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float beta) @@ -45,13 +63,32 @@ void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayer::validate(input->info(), output->info())); + _needs_flattening = input->info()->num_dimensions() > 2; + + // If we are dealing with a 4D tensor, we will: + // - Flatten the input, so that we end up with a [width*height*depth] * batches 2D tensor + // - Execute all the pipeline (reduction + normalization) on the flattened tensor + // - Reshape the flattened output into the real output + if(_needs_flattening) + { + // Add to the memory manager _input_flat + _memory_group.manage(&_input_flat); + + // Cofigure _flatten_kernel and _input_flat + configure_flatten_kernel(input, output); + } + + // We want to deal with a 2D input. Either it is the flattened version of the original input (4D case) + // or it is the original input case (2D case) + const ICLTensor *input_2D = (_needs_flattening ? &_input_flat : input); + // Create intermediate tensors shapes - const TensorInfo input_info = input->info()->clone()->reset_padding().set_is_resizable(true); - DataType tmp_data_type = is_data_type_quantized_asymmetric(input->info()->data_type()) ? DataType::S32 : input->info()->data_type(); - TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type)); + TensorInfo input_info = input_2D->info()->clone()->reset_padding().set_is_resizable(true); + DataType tmp_data_type = is_data_type_quantized_asymmetric(input_2D->info()->data_type()) ? DataType::S32 : input_2D->info()->data_type(); + TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type)); _tmp.allocator()->init(tensor_info_tmp); - TensorShape max_sum_shape = input->info()->tensor_shape(); + TensorShape max_sum_shape = input_2D->info()->tensor_shape(); max_sum_shape.set(0, 1); _max.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape)); _sum.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type)); @@ -65,8 +102,28 @@ void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float _memory_group.manage(&_sum); // Configure kernels - _max_shift_exp_sum_kernel.configure(input, &_max, &_tmp, &_sum, beta); - _norm_kernel.configure(&_tmp, &_sum, output, beta); + _max_shift_exp_sum_kernel.configure(input_2D, &_max, &_tmp, &_sum, beta); + + if(_needs_flattening) + { + // Add to the memory manager _output_flat + _memory_group.manage(&_output_flat); + + // The normalization kernel stores the result in a flat output tensor + _norm_kernel.configure(&_tmp, &_sum, &_output_flat, beta); + + // Reshape the flat output into a the requested (4D) output + _reshape_kernel.configure(&_output_flat, output); + + // Allocate the intermediate flat tensors + _input_flat.allocator()->allocate(); + _output_flat.allocator()->allocate(); + } + else + { + // Softmax 2D case + _norm_kernel.configure(&_tmp, &_sum, output, beta); + } // Allocate intermediate buffers _tmp.allocator()->allocate(); @@ -77,7 +134,7 @@ void CLSoftmaxLayer::configure(const ICLTensor *input, ICLTensor *output, float Status CLSoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *output) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 2, "Only 2D inputs are supported"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 4, "Only up to 4 dimensions are supported"); // Create intermediate tensor info DataType tmp_data_type = is_data_type_quantized_asymmetric(input->data_type()) ? DataType::S32 : input->data_type(); @@ -88,6 +145,14 @@ Status CLSoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *out TensorInfo tensor_info_max(input->clone()->set_tensor_shape(max_sum_shape).set_is_resizable(true)); TensorInfo tensor_info_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(QuantizationInfo()).set_is_resizable(true)); + const TensorShape shape_flatten = misc::shape_calculator::compute_flatten_shape(input); + TensorInfo tensor_info_flat(input->clone()->set_tensor_shape(shape_flatten).set_is_resizable(true)); + + if(input->num_dimensions() > 2) // needs flattening + { + ARM_COMPUTE_RETURN_ON_ERROR(CLFlattenLayerKernel::validate(input, &tensor_info_flat)); + } + ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DMaxShiftExpSumKernel::validate(input, &tensor_info_max, &tensor_info_tmp, &tensor_info_sum)); ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DNormKernel::validate(&tensor_info_tmp, &tensor_info_sum, output)); @@ -97,9 +162,21 @@ Status CLSoftmaxLayer::validate(const ITensorInfo *input, const ITensorInfo *out void CLSoftmaxLayer::run() { _memory_group.acquire(); + if(_needs_flattening) + { + CLScheduler::get().enqueue(_flatten_kernel, false); + } CLScheduler::get().enqueue(_max_shift_exp_sum_kernel, false); - CLScheduler::get().enqueue(_norm_kernel); + CLScheduler::get().enqueue(_norm_kernel, !_needs_flattening); + if(_needs_flattening) + { + CLScheduler::get().enqueue(_reshape_kernel, true); + } + + // Relase intermediate buffers _memory_group.release(); } + +} // namespace arm_compute -- cgit v1.2.1